An integrated soft and hard classification approach for evaluating urban expansion from multisource remote sensing data: a case study of the Beijing–Tianjin–Tangshan metropolitan region, China. Issue 11 (3rd June 2018)

• Record Type: Journal Article
• Authors: Cao, Shisong; Hu, Deyong; Hu, Zhuowei; Zhao, Wenji; Mo, You; Qiao, Kun
• Is Part Of: International journal of remote sensing. Volume 39:Issue 11(2018)
• Page Start: 3556
• Page End: 3579
• Publication Date: 2018-06-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1444291
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Integrating soft and hard classification to monitor urban expansion can effectively provide comprehensive urban growth information to urban planners. In this study, both the impervious surface coverage (as a soft classification result) and land cover (as a hard classification result) in the Beijing–Tianjin–Tangshan metropolitan region (BTTMR), China, were extracted from multisource remote sensing data from 1990 to 2015. Then, we evaluated urban expansion based on centre migration, standard deviation ellipse, and spatial autocorrelation metrics. Furthermore, the differences between the soft and hard classification results were analysed at the landscape scale. The results showed that (1) the impervious surface area increased considerably over the past 25 years. Notably, the areas of urban built-up land and industrial production land increased rapidly, while those of ecological land and agricultural production land seriously decreased. (2) The distribution of impervious surfaces was closely related to the regional economic development plan of 'One Axis, Two Wing, and Multi-Node' in the BTTMR. (3) The contributions of different land use types to impervious surface growth ranked from high to low as follows: urban built-up land, rural residential land, industrial production land, agricultural production land, and ecological land. (4) The landscape metrics varied considerably based on the hard and soft classification results and were sensitive to different factors.